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An efficient on-chip learning method based on neuron multiplexing is proposed in this paper to address the limitations of traditional on-chip learning methods, including low resource utilization and non-tunable parallelism. The proposed method utilizes a configurable neuron calculation unit (NCU) to calculate neural networks in different degrees of parallelism through multiplexing NCUs at different levels, and resource utilization can be increased by reducing the number of NCUs since the resource consumption is predominantly determined by the number of NCUs and the data bit-width, which are decoupled from the specific topology. To better support the proposed method and minimize RAM block usage, a weight segmentation and recombination method is introduced, accompanied by a detailed explanation of the access order. Moreover, a performance model is developed to facilitate parameter selection process. Experimental results conducted on an FPGA development board demonstrate that the proposed method has lower resource consumption, higher resource utilization, and greater generality compared to other methods. [ABSTRACT FROM AUTHOR]

Background: Accumulated evidence highlights the significance of the crosstalk between epigenetic and epitranscriptomic mechanisms, notably 5-methylcytosine (5mC) and N6-methyladenosine (m6A). Herein, we conducted a widespread analysis regarding the crosstalk between 5mC and m6A regulators in hepatocellular carcinoma (HCC). Methods: Pan-cancer genomic analysis of the crosstalk between 5mC and m6A regulators was presented at transcriptomic, genomic, epigenetic, and other multi-omics levels. Hub 5mC and m6A regulators were summarized to define an epigenetic and epitranscriptomic module eigengene (EME), which reflected both the pre- and post-transcriptional modifications. Results: 5mC and m6A regulators interacted with one another at the multi-omic levels across pan-cancer, including HCC. The EME scoring system enabled to greatly optimize risk stratification and accurately predict HCC patients' clinical outcomes and progression. Additionally, the EME accurately predicted the responses to mainstream therapies (TACE and sorafenib) and immunotherapy as well as hyper-progression. In vitro, 5mC and m6A regulators cooperatively weakened apoptosis and facilitated proliferation, DNA damage repair, G2/M arrest, migration, invasion and epithelial-to-mesenchymal transition (EMT) in HCC cells. The EME scoring system was remarkably linked to potential extrinsic and intrinsic immune escape mechanisms, and the high EME might contribute to a reduced copy number gain/loss frequency. Finally, we determined potential therapeutic compounds and druggable targets (TUBB1 and P2RY4) for HCC patients with high EME. Conclusions: Our findings suggest that HCC may result from a unique synergistic combination of 5mC-epigenetic mechanism mixed with m6A-epitranscriptomic mechanism, and their crosstalk defines therapeutic response and pharmacogenomic landscape. [ABSTRACT FROM AUTHOR]

Some steganography methods for gray-scale image can be extended to true RGB color image by treating each of its three color channels as a gray-scale image. In modern popular steganography, most embedding changes are highly concentrated on those complex textural regions with smaller embedding distortions. However, the existing steganalysis methods for color images directly extract steganalytic features from the whole image. In this paper, we propose a content-adaptive steganalysis strategy for color images. The new strategy aims to extract spatial rich model features from each color channel and just extract color rich model features from the pixels that may have been modified. In order to locate those suspected pixels, we first calculate the embedding costs of each channel, and then a subset of pixels with smaller embedding costs is selected. Experimental results show that the proposed strategy performs better than the state-of-the-art color image steganalysis method. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objectives: T‐cell immunoglobulin domain and mucin domain‐4 (TIM‐4) is selectively expressed on antigen‐presenting cells (APCs) and modulates various immune responses. However, the role of TIM‐4 expressed by Kupffer cells (KCs) in liver fibrosis remains unclear. The present study aimed to explore whether and how TIM‐4 expressed by KCs is involved in liver fibrosis. Materials and Methods: Mice chronic liver fibrosis models were established and divided into the olive‐induced control group, CCL4‐induced control group, olive‐induced TIM‐4 interference group and CCL4‐induced TIM‐4 interference group. Different techniques were used to monitor the fibrotic effects of TIM‐4, including histopathological assays, Western blotting, ELISA and transmission electron microscopy. Additionally, mice liver transplant models were established to determine the fibrotic effects of TIM‐4 on fibrosis after liver transplantation (LT). Results: We found that the induction of liver fibrosis by CCL4 was associated with TIM‐4 expression in KCs. TIM‐4 interference essentially contributed to liver fibrosis resolution. KCs from the TIM‐4 interference group had decreased levels of pro‐fibrotic markers, reduced TGF‐β1 secretion and inhibited hepatic stellate cell (HSC) differentiation into myofibroblast‐like cells. In addition, we used GdCl3 to verify that KCs are the primary source of TGF‐β1 during fibrosis progression. Moreover, KCs from CCL4‐induced mice showed increased ROS production, mitophagy activation and TGF‐β1 secretion. However, TIM‐4 interference in the KCs inhibited Akt1‐mediated ROS production, resulting in the suppression of PINK1, Parkin and LC3‐II/I activation and the reduction of TGF‐β1 secretion during liver fibrosis. Additionally, TIM‐4 interference potentially attenuated development of fibrosis after LT. Conclusions: Our findings revealed the underlying mechanisms of TIM‐4 interference in KCs to mitigate liver fibrosis. [ABSTRACT FROM AUTHOR]

Objectives: The prevention and treatment of liver transplant rejection remain challenging. We investigated the pathophysiological mechanisms of liver transplant rejection in rats and screened candidate genes to determine their degree of rejection response for possible development of potential therapeutic targets., Materials and Methods: Brown Norway-Brown Norway transplant tolerant models and Lewis-Brown Norway transplant rejection models were established. We collected liver tissue and venous blood at 7 days posttransplant for hematoxylin and eosin staining and RNA sequencing analysis, respectively. We conducted differential expression gene analysis, KEGG and GO enrichment analysis. We performed immunohistochemistry to detect highly expressed immunerelated proteins, including lymphocyte-specific protein tyrosine kinase, linker for activation of T cells, and 70-kDa T-cell receptor zeta-chain-associated protein kinase., Results: Significant differences were found in liver function and Banff scores between rejection and tolerant groups, indicating the successful establishment of liver transplant models. RNA-sequencing screened 7521 differentially expressed genes, with 3355 upregulated and 3058 downregulated. KEGG analysis of upregulated genes showed that 8 of the top 20 enrichment pathways were associated with immune system processes and 5 were related to immune system diseases. Among these immune pathways, 289 genes were upregulated; of these, 147 genes were removed after comparison with the IMMPORT database, of which 97 genes were significantly changed. Our GO analysis showed upregulated genes mainly participating in immune response processes, with downregulated genes mainly participating in metabolic processes. Real-time polymerase chain reaction and immunohistochemistry verified expression of the immune-related proteins, consistent with RNAsequencing results, which were mainly expressed in inflammatory cells in sinus and portal vein., Conclusions: Immune-related genes were found to be associated with liver transplant rejection. The 3 immune-related genes that we analyzed may play a role in liver transplant rejection and can possibly serve as candidate markers for monitoring the degree of liver transplant rejection.

Background: Inserting diffusion weighted imaging (DWI) into the time interval between post contrast and hepatobiliary phase (HBP) is time saving and health economic friendly. However, whether DWI would be affected before and after Gd-EOB-DTPA is still unknown. This study aims to validate whether the DWI at both low and high b-values is affected before and after Gd-EOB-DTPA enhancement., Methods: From July 2019 to November 2019, seventy-three patients who satisfied the inclusion criteria were enrolled. Those patients were scanned with multiple b-value (b-value of 0, 50, 800, 1,000, and 1,200 s/mm 2 ) DWI using a 3.0 T magnetic resonance (MR) scanner before and after the injection of Gd-EOB-DTPA. The final imaging diagnosis of the malignant liver lesions were made by histopathological analysis. The lesion-liver contrast intensity ratio (CIR) and the apparent diffusion coefficients (ADCs) of hepatic parenchyma and lesions at each b-value was evaluated. The Student's t -test or Mann-Whitney U test was used to compare the CIR and ADC between the MR images before and after contrast agent injection. In addition, the Student's t -test or Mann-Whitney U test was used to compare the ADC values between benign and malignant lesions. Receiver operating characteristics (ROC) curves were used to assess the area under the curve (AUC) of the ADC values in differentiating between benign and malignant lesions., Results: For the CIRs comparison, the CIRs showed no statistical significance before and after Gd-EOB-DTPA on b =0 (1.34±1.15 vs. 1.45±1.48, P=0.664), b=50 (1.23±1.13 vs. 1.35±1.34, P=0.982), b=800 (1.19±0.87 vs. 1.19±0.94, P=0.946), b=1,000 (1.21±0.90 vs. 1.32±1.05, P=0.294) and b=1,200 (1.25±1.03 vs. 1.45±1.48, P=0.165) s/mm 2 . For the ADC value comparison, the ADC also showed no statistical significance before and after Gd-EOB-DTPA on b=50 (4.04±2.82 vs. 3.91±3.00, P=0.151), b=800 (1.68±0.71 vs. 1.67±0.76, P=0.163), b=1,000 (1.53±0.69 vs. 1.50±0.70, P=0.078) and b=1,200 (1.48±0.66 vs. 1.48±0.70, P=0.294) s/mm 2 ., Conclusions: DWI scanned between the interval of dynamic enhanced imaging and HBP imaging can save overall scanning time without influencing the CIRs, ADCs, and diagnostic capabilities of hepatic lesions at both low and high b-values., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-962/coif). LN and XW are from GE Healthcare China. The other authors have no conflicts of interest to declare., (2022 Annals of Translational Medicine. All rights reserved.)